{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# infusionDrug\n",
    "\n",
    "Continuous infusions are documented here and are entered from the nursing flowsheet (either manually or interfaced from the hospital electronic health record system).\n",
    "\n",
    "Infusion drugs entered directly into the source system (eCareManager) by clinicians must include the concentration of the drug being infused. This is done by entering the \"drugAmount\" and \"volumeOfFluid\" and this is independent of the amount being infused (drugRate or infusionRate). Interfaced values from source EMRs may not contain the concentration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "import pdvega\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine a single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "patientunitstayid = 242380"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>infusiondrugid</th>\n",
       "      <th>infusionyear</th>\n",
       "      <th>infusiontime24</th>\n",
       "      <th>infusiontime</th>\n",
       "      <th>infusionoffset</th>\n",
       "      <th>drugname</th>\n",
       "      <th>drugrate</th>\n",
       "      <th>infusionrate</th>\n",
       "      <th>drugamount</th>\n",
       "      <th>volumeoffluid</th>\n",
       "      <th>patientweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>242380</td>\n",
       "      <td>2001050</td>\n",
       "      <td>2015</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>59</td>\n",
       "      <td>Nitroglycerin (mcg/min)</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>250</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>242380</td>\n",
       "      <td>1953142</td>\n",
       "      <td>2015</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>evening</td>\n",
       "      <td>59</td>\n",
       "      <td>Heparin (units/hr)</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>25000</td>\n",
       "      <td>500</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>242380</td>\n",
       "      <td>1951808</td>\n",
       "      <td>2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>night</td>\n",
       "      <td>239</td>\n",
       "      <td>Heparin (units/hr)</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>25000</td>\n",
       "      <td>500</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>242380</td>\n",
       "      <td>1973839</td>\n",
       "      <td>2015</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>night</td>\n",
       "      <td>239</td>\n",
       "      <td>Nitroglycerin (mcg/min)</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>250</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>242380</td>\n",
       "      <td>1985551</td>\n",
       "      <td>2015</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>night</td>\n",
       "      <td>419</td>\n",
       "      <td>Heparin (units/hr)</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>25000</td>\n",
       "      <td>500</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patientunitstayid  infusiondrugid  infusionyear infusiontime24  \\\n",
       "0             242380         2001050          2015       15:00:00   \n",
       "1             242380         1953142          2015       15:00:00   \n",
       "2             242380         1951808          2015       18:00:00   \n",
       "3             242380         1973839          2015       18:00:00   \n",
       "4             242380         1985551          2015       21:00:00   \n",
       "\n",
       "  infusiontime  infusionoffset                 drugname drugrate infusionrate  \\\n",
       "0      evening              59  Nitroglycerin (mcg/min)       10            3   \n",
       "1      evening              59       Heparin (units/hr)     1000           20   \n",
       "2        night             239       Heparin (units/hr)     1000           20   \n",
       "3        night             239  Nitroglycerin (mcg/min)       20            6   \n",
       "4        night             419       Heparin (units/hr)     1000           20   \n",
       "\n",
       "  drugamount volumeoffluid patientweight  \n",
       "0         50           250                \n",
       "1      25000           500                \n",
       "2      25000           500                \n",
       "3         50           250                \n",
       "4      25000           500                "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select *\n",
    "from infusiondrug\n",
    "where patientunitstayid = {}\n",
    "order by infusionoffset\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we can see a patient is admitted on (or quickly administered after admission) two drugs: nitroglycerin and heparin. The `drugrate` contains the infusion rate (1000 units/hr for herparin, 10 mcg/min for nitroglycerin). Further, the `drugamount` and `volumeoffluid` likely indicate the contents of the bag holding the fluid: a 500 mL bag containing a total of 25,000 units for heparin, and a 250 mL bag containing a total of 50 mg of nitroglycerin (note the unit change between the rate/amount in the nitroglycerin bag).\n",
    "\n",
    "Sometimes patient drugs are dosed by weight - in those cases, the `patientweight` column would be populated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>data completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>188</td>\n",
       "      <td>3601</td>\n",
       "      <td>3314</td>\n",
       "      <td>92.029992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>3134</td>\n",
       "      <td>66.980124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>2738</td>\n",
       "      <td>64.529814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>2533</td>\n",
       "      <td>59.223755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>2461</td>\n",
       "      <td>40.397242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>281</td>\n",
       "      <td>2531</td>\n",
       "      <td>2244</td>\n",
       "      <td>88.660608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>300</td>\n",
       "      <td>3617</td>\n",
       "      <td>1827</td>\n",
       "      <td>50.511474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>283</td>\n",
       "      <td>2097</td>\n",
       "      <td>1805</td>\n",
       "      <td>86.075346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>176</td>\n",
       "      <td>4328</td>\n",
       "      <td>1793</td>\n",
       "      <td>41.427911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>252</td>\n",
       "      <td>3371</td>\n",
       "      <td>1539</td>\n",
       "      <td>45.654109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "65          188                3601                         3314   \n",
       "184         420                4679                         3134   \n",
       "90          243                4243                         2738   \n",
       "134         338                4277                         2533   \n",
       "54          167                6092                         2461   \n",
       "119         281                2531                         2244   \n",
       "122         300                3617                         1827   \n",
       "121         283                2097                         1805   \n",
       "58          176                4328                         1793   \n",
       "98          252                3371                         1539   \n",
       "\n",
       "     data completion  \n",
       "65         92.029992  \n",
       "184        66.980124  \n",
       "90         64.529814  \n",
       "134        59.223755  \n",
       "54         40.397242  \n",
       "119        88.660608  \n",
       "122        50.511474  \n",
       "121        86.075346  \n",
       "58         41.427911  \n",
       "98         45.654109  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct d.patientunitstayid) as number_of_patients_with_tbl\n",
    "from patient pt\n",
    "left join infusiondrug d\n",
    "  on pt.patientunitstayid = d.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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"
     },
     "metadata": {
      "jupyter-vega3": "#ca5f6394-aa13-41c7-86e3-e0f258b1446a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['data completion']].vgplot.hist(bins=10,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is common in eICU-CRD, there are a subset of hospitals who routinely utilize this portion of the medical record (and thus have high data completion), while there are other hospitals who rarely use this interface and thus have poor data completion.\n",
    "\n",
    "The infusionDrug appears to be less frequently used, with over 100 hospitals having no data documented in the table. Conversely, over 40 hospitals have data in this table for all patients admitted."
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
